Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f7a7561a240>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f7a755067f0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [24]:
def discriminator(images, reuse=False, alpha=0.1):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        # 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256 ?
        
        x4 = tf.layers.conv2d(relu3, 512, 5, strides=1, padding='same')
        bn4 = tf.layers.batch_normalization(x4, training=True)
        relu4 = tf.maximum(alpha * bn4, bn4)
        # 4x4x512 ?
        
        # Flatten it
        flat = tf.reshape(relu4, (-1, 4*4*512))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.1):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x512 now
        
        x1 = tf.image.resize_nearest_neighbor(x1, (7,7))
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=1, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256 now
        
        x2 = tf.image.resize_nearest_neighbor(x2, (14,14))
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=1, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 14x14x128 now

        x3 = tf.image.resize_nearest_neighbor(x3, (28,28))
        
        x4 = tf.layers.conv2d_transpose(x3, 64, 5, strides=1, padding='same')
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(alpha * x4, x4)
        # 28x28x64 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 3, strides=1, padding='same')
        # 28x28x3 now
        
        out = tf.tanh(logits)
        
        return out  
    return None


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [12]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    # Add noise to the descriminator labels
    epsilon_real = tf.random_uniform(
        shape=tf.shape(d_model_real),
        minval=-0.2,
        maxval=0.2,
        dtype=tf.float32,
        seed=1337,
    )
    
    epsilon_fake = tf.random_uniform(
        shape=tf.shape(d_model_fake),        
        minval=0.0,
        maxval=0.2,
        dtype=tf.float32,
        seed=1337,
    )
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) + epsilon_real))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake) + epsilon_fake))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [13]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed
In [14]:
class GAN:
    def __init__(self, real_dim, z_dim, learning_rate, beta1=0.5):
#         tf.reset_default_graph()        
        self.input_real, self.input_z, self.learning_rate = model_inputs(real_dim[1], real_dim[2], real_dim[3], z_dim)
        self.d_loss, self.g_loss = model_loss(self.input_real, self.input_z, real_dim[-1])
        self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, learning_rate, beta1)

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [15]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [16]:
!mkdir checkpoints
mkdir: cannot create directory ‘checkpoints’: File exists
In [20]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    net = GAN(data_shape, z_dim, learning_rate=learning_rate, beta1=beta1)

#     saver = tf.train.Saver()
    steps = 0 
    with tf.Session() as sess:

        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_real in get_batches(batch_size):                
                steps += 1
                
                # scale to [-1, 1]
                batch_real *= 2

                # Sample random noise for G
                batch_z = np.random.normal(size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(net.d_opt, feed_dict={
                    net.input_real: batch_real, 
                    net.input_z: batch_z,
                    net.learning_rate: learning_rate
                })
                _ = sess.run(net.g_opt, feed_dict={
                    net.input_real: batch_real,
                    net.input_z: batch_z,
                    net.learning_rate: learning_rate
                })


                if steps % 10 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = net.d_loss.eval({net.input_z: batch_z, net.input_real: batch_real})
                    train_loss_g = net.g_loss.eval({net.input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, 36, net.input_z, data_shape[-1], data_image_mode)
#         saver.save(sess, './checkpoints/generator.ckpt')

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [28]:
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.2342... Generator Loss: 3.3154
Epoch 1/2... Discriminator Loss: 3.8042... Generator Loss: 2.5365
Epoch 1/2... Discriminator Loss: 2.2983... Generator Loss: 0.6894
Epoch 1/2... Discriminator Loss: 1.9233... Generator Loss: 1.1458
Epoch 1/2... Discriminator Loss: 1.6819... Generator Loss: 0.8725
Epoch 1/2... Discriminator Loss: 2.1323... Generator Loss: 0.1254
Epoch 1/2... Discriminator Loss: 1.8550... Generator Loss: 0.1884
Epoch 1/2... Discriminator Loss: 1.6064... Generator Loss: 0.2409
Epoch 1/2... Discriminator Loss: 1.3791... Generator Loss: 0.3881
Epoch 1/2... Discriminator Loss: 2.1952... Generator Loss: 0.1000
Epoch 1/2... Discriminator Loss: 1.6605... Generator Loss: 0.2306
Epoch 1/2... Discriminator Loss: 1.7587... Generator Loss: 0.1964
Epoch 1/2... Discriminator Loss: 1.6393... Generator Loss: 0.2419
Epoch 1/2... Discriminator Loss: 1.1698... Generator Loss: 0.6249
Epoch 1/2... Discriminator Loss: 1.3178... Generator Loss: 1.0599
Epoch 1/2... Discriminator Loss: 1.1731... Generator Loss: 0.5675
Epoch 1/2... Discriminator Loss: 1.1340... Generator Loss: 0.8936
Epoch 1/2... Discriminator Loss: 1.4024... Generator Loss: 0.3485
Epoch 1/2... Discriminator Loss: 1.6906... Generator Loss: 0.2024
Epoch 1/2... Discriminator Loss: 1.5252... Generator Loss: 0.2719
Epoch 1/2... Discriminator Loss: 1.5911... Generator Loss: 0.2494
Epoch 1/2... Discriminator Loss: 1.5768... Generator Loss: 0.2941
Epoch 1/2... Discriminator Loss: 1.7391... Generator Loss: 0.2125
Epoch 1/2... Discriminator Loss: 1.4716... Generator Loss: 0.2758
Epoch 1/2... Discriminator Loss: 1.6769... Generator Loss: 0.2175
Epoch 1/2... Discriminator Loss: 1.5995... Generator Loss: 0.2383
Epoch 1/2... Discriminator Loss: 1.4117... Generator Loss: 0.3666
Epoch 1/2... Discriminator Loss: 1.4124... Generator Loss: 0.3317
Epoch 1/2... Discriminator Loss: 1.6255... Generator Loss: 0.2315
Epoch 1/2... Discriminator Loss: 1.5232... Generator Loss: 0.2907
Epoch 1/2... Discriminator Loss: 1.5444... Generator Loss: 0.2642
Epoch 1/2... Discriminator Loss: 1.5816... Generator Loss: 0.2448
Epoch 1/2... Discriminator Loss: 1.4317... Generator Loss: 0.3198
Epoch 1/2... Discriminator Loss: 1.4433... Generator Loss: 0.3300
Epoch 1/2... Discriminator Loss: 1.1845... Generator Loss: 1.0294
Epoch 1/2... Discriminator Loss: 1.2900... Generator Loss: 0.4813
Epoch 1/2... Discriminator Loss: 1.2354... Generator Loss: 0.5533
Epoch 1/2... Discriminator Loss: 1.1852... Generator Loss: 0.4878
Epoch 1/2... Discriminator Loss: 1.3587... Generator Loss: 0.3401
Epoch 1/2... Discriminator Loss: 1.3683... Generator Loss: 1.3751
Epoch 1/2... Discriminator Loss: 1.0053... Generator Loss: 0.6517
Epoch 1/2... Discriminator Loss: 1.3763... Generator Loss: 0.3674
Epoch 1/2... Discriminator Loss: 1.3635... Generator Loss: 0.3479
Epoch 1/2... Discriminator Loss: 1.1743... Generator Loss: 0.7755
Epoch 1/2... Discriminator Loss: 1.1894... Generator Loss: 1.0942
Epoch 1/2... Discriminator Loss: 1.1940... Generator Loss: 0.6811
Epoch 1/2... Discriminator Loss: 1.5769... Generator Loss: 0.2259
Epoch 1/2... Discriminator Loss: 1.1963... Generator Loss: 0.5591
Epoch 1/2... Discriminator Loss: 1.2660... Generator Loss: 0.3768
Epoch 1/2... Discriminator Loss: 1.1381... Generator Loss: 0.7225
Epoch 1/2... Discriminator Loss: 1.1093... Generator Loss: 0.7590
Epoch 1/2... Discriminator Loss: 1.1939... Generator Loss: 1.3170
Epoch 1/2... Discriminator Loss: 1.1815... Generator Loss: 0.5376
Epoch 1/2... Discriminator Loss: 1.2483... Generator Loss: 0.9242
Epoch 1/2... Discriminator Loss: 1.0494... Generator Loss: 0.7168
Epoch 1/2... Discriminator Loss: 1.6317... Generator Loss: 0.2632
Epoch 1/2... Discriminator Loss: 1.2293... Generator Loss: 0.4225
Epoch 1/2... Discriminator Loss: 1.2876... Generator Loss: 1.6494
Epoch 1/2... Discriminator Loss: 1.0286... Generator Loss: 0.5719
Epoch 1/2... Discriminator Loss: 1.3333... Generator Loss: 0.3495
Epoch 1/2... Discriminator Loss: 1.5521... Generator Loss: 0.2725
Epoch 1/2... Discriminator Loss: 1.8120... Generator Loss: 0.1738
Epoch 1/2... Discriminator Loss: 1.0903... Generator Loss: 0.7035
Epoch 1/2... Discriminator Loss: 1.3345... Generator Loss: 0.3257
Epoch 1/2... Discriminator Loss: 1.0625... Generator Loss: 0.9339
Epoch 1/2... Discriminator Loss: 1.1521... Generator Loss: 0.5732
Epoch 1/2... Discriminator Loss: 1.0024... Generator Loss: 1.2962
Epoch 1/2... Discriminator Loss: 1.0959... Generator Loss: 0.6682
Epoch 1/2... Discriminator Loss: 1.3251... Generator Loss: 0.3375
Epoch 1/2... Discriminator Loss: 1.2907... Generator Loss: 0.3351
Epoch 1/2... Discriminator Loss: 1.3101... Generator Loss: 0.4385
Epoch 1/2... Discriminator Loss: 1.0414... Generator Loss: 0.5899
Epoch 1/2... Discriminator Loss: 0.9793... Generator Loss: 1.6919
Epoch 1/2... Discriminator Loss: 1.0464... Generator Loss: 0.9048
Epoch 1/2... Discriminator Loss: 1.9746... Generator Loss: 2.1962
Epoch 1/2... Discriminator Loss: 1.3757... Generator Loss: 0.3650
Epoch 1/2... Discriminator Loss: 1.1416... Generator Loss: 0.5985
Epoch 1/2... Discriminator Loss: 1.2340... Generator Loss: 0.4428
Epoch 1/2... Discriminator Loss: 1.6708... Generator Loss: 0.2196
Epoch 1/2... Discriminator Loss: 1.3085... Generator Loss: 0.4092
Epoch 1/2... Discriminator Loss: 1.3768... Generator Loss: 1.8470
Epoch 1/2... Discriminator Loss: 1.3766... Generator Loss: 0.3713
Epoch 1/2... Discriminator Loss: 1.0820... Generator Loss: 0.5171
Epoch 1/2... Discriminator Loss: 0.9400... Generator Loss: 0.6789
Epoch 1/2... Discriminator Loss: 1.8714... Generator Loss: 0.1911
Epoch 1/2... Discriminator Loss: 1.4060... Generator Loss: 0.3558
Epoch 1/2... Discriminator Loss: 1.2228... Generator Loss: 0.4225
Epoch 1/2... Discriminator Loss: 0.9078... Generator Loss: 1.2691
Epoch 1/2... Discriminator Loss: 1.0455... Generator Loss: 0.5562
Epoch 1/2... Discriminator Loss: 0.9184... Generator Loss: 0.7952
Epoch 1/2... Discriminator Loss: 1.1746... Generator Loss: 0.5349
Epoch 1/2... Discriminator Loss: 0.9349... Generator Loss: 1.1784
Epoch 1/2... Discriminator Loss: 1.9873... Generator Loss: 1.6611
Epoch 2/2... Discriminator Loss: 1.3287... Generator Loss: 0.4159
Epoch 2/2... Discriminator Loss: 0.9689... Generator Loss: 0.6218
Epoch 2/2... Discriminator Loss: 1.3928... Generator Loss: 0.3770
Epoch 2/2... Discriminator Loss: 0.8579... Generator Loss: 0.9392
Epoch 2/2... Discriminator Loss: 0.8190... Generator Loss: 1.4005
Epoch 2/2... Discriminator Loss: 0.9653... Generator Loss: 0.8200
Epoch 2/2... Discriminator Loss: 0.7692... Generator Loss: 1.2551
Epoch 2/2... Discriminator Loss: 2.5392... Generator Loss: 0.0912
Epoch 2/2... Discriminator Loss: 1.2596... Generator Loss: 0.5345
Epoch 2/2... Discriminator Loss: 0.8960... Generator Loss: 0.8572
Epoch 2/2... Discriminator Loss: 1.4012... Generator Loss: 0.3353
Epoch 2/2... Discriminator Loss: 1.1092... Generator Loss: 0.6442
Epoch 2/2... Discriminator Loss: 0.8982... Generator Loss: 0.8999
Epoch 2/2... Discriminator Loss: 0.8600... Generator Loss: 0.9945
Epoch 2/2... Discriminator Loss: 0.7526... Generator Loss: 1.2196
Epoch 2/2... Discriminator Loss: 0.8926... Generator Loss: 1.7912
Epoch 2/2... Discriminator Loss: 0.8064... Generator Loss: 0.6574
Epoch 2/2... Discriminator Loss: 0.9074... Generator Loss: 0.7713
Epoch 2/2... Discriminator Loss: 0.9255... Generator Loss: 1.2718
Epoch 2/2... Discriminator Loss: 1.0609... Generator Loss: 0.5078
Epoch 2/2... Discriminator Loss: 0.7143... Generator Loss: 1.1615
Epoch 2/2... Discriminator Loss: 1.7070... Generator Loss: 0.2370
Epoch 2/2... Discriminator Loss: 1.1177... Generator Loss: 0.4693
Epoch 2/2... Discriminator Loss: 0.8703... Generator Loss: 0.9155
Epoch 2/2... Discriminator Loss: 0.9839... Generator Loss: 0.6094
Epoch 2/2... Discriminator Loss: 0.9069... Generator Loss: 0.7840
Epoch 2/2... Discriminator Loss: 0.8327... Generator Loss: 1.3230
Epoch 2/2... Discriminator Loss: 1.9129... Generator Loss: 0.1812
Epoch 2/2... Discriminator Loss: 0.8478... Generator Loss: 1.2454
Epoch 2/2... Discriminator Loss: 0.7565... Generator Loss: 1.6676
Epoch 2/2... Discriminator Loss: 0.5542... Generator Loss: 1.2068
Epoch 2/2... Discriminator Loss: 0.8597... Generator Loss: 0.6836
Epoch 2/2... Discriminator Loss: 1.8040... Generator Loss: 0.1740
Epoch 2/2... Discriminator Loss: 0.9704... Generator Loss: 0.6369
Epoch 2/2... Discriminator Loss: 2.0069... Generator Loss: 0.2032
Epoch 2/2... Discriminator Loss: 1.3297... Generator Loss: 0.4053
Epoch 2/2... Discriminator Loss: 0.8929... Generator Loss: 0.7404
Epoch 2/2... Discriminator Loss: 0.8544... Generator Loss: 1.0003
Epoch 2/2... Discriminator Loss: 1.2423... Generator Loss: 0.4265
Epoch 2/2... Discriminator Loss: 0.8304... Generator Loss: 1.2803
Epoch 2/2... Discriminator Loss: 0.8274... Generator Loss: 0.7930
Epoch 2/2... Discriminator Loss: 0.7842... Generator Loss: 0.9558
Epoch 2/2... Discriminator Loss: 1.6908... Generator Loss: 0.2211
Epoch 2/2... Discriminator Loss: 0.8768... Generator Loss: 0.7361
Epoch 2/2... Discriminator Loss: 0.9874... Generator Loss: 0.5784
Epoch 2/2... Discriminator Loss: 0.8831... Generator Loss: 0.7754
Epoch 2/2... Discriminator Loss: 1.1659... Generator Loss: 1.6319
Epoch 2/2... Discriminator Loss: 0.9402... Generator Loss: 0.6274
Epoch 2/2... Discriminator Loss: 1.4415... Generator Loss: 0.3116
Epoch 2/2... Discriminator Loss: 1.1922... Generator Loss: 0.3718
Epoch 2/2... Discriminator Loss: 0.8051... Generator Loss: 1.5304
Epoch 2/2... Discriminator Loss: 1.4115... Generator Loss: 0.3342
Epoch 2/2... Discriminator Loss: 1.5052... Generator Loss: 0.2618
Epoch 2/2... Discriminator Loss: 0.9227... Generator Loss: 0.8677
Epoch 2/2... Discriminator Loss: 0.9308... Generator Loss: 1.1747
Epoch 2/2... Discriminator Loss: 0.8726... Generator Loss: 1.0088
Epoch 2/2... Discriminator Loss: 1.5477... Generator Loss: 0.2620
Epoch 2/2... Discriminator Loss: 1.0454... Generator Loss: 0.7113
Epoch 2/2... Discriminator Loss: 0.7421... Generator Loss: 1.8034
Epoch 2/2... Discriminator Loss: 0.9169... Generator Loss: 1.2225
Epoch 2/2... Discriminator Loss: 0.8347... Generator Loss: 0.9241
Epoch 2/2... Discriminator Loss: 0.9539... Generator Loss: 0.9814
Epoch 2/2... Discriminator Loss: 0.8219... Generator Loss: 0.7592
Epoch 2/2... Discriminator Loss: 0.9368... Generator Loss: 1.7894
Epoch 2/2... Discriminator Loss: 1.0304... Generator Loss: 0.5228
Epoch 2/2... Discriminator Loss: 0.9384... Generator Loss: 0.7166
Epoch 2/2... Discriminator Loss: 1.2161... Generator Loss: 0.4642
Epoch 2/2... Discriminator Loss: 2.1955... Generator Loss: 0.1270
Epoch 2/2... Discriminator Loss: 0.8266... Generator Loss: 1.0908
Epoch 2/2... Discriminator Loss: 0.9160... Generator Loss: 0.8257
Epoch 2/2... Discriminator Loss: 1.2781... Generator Loss: 0.4193
Epoch 2/2... Discriminator Loss: 0.9447... Generator Loss: 0.6367
Epoch 2/2... Discriminator Loss: 0.7939... Generator Loss: 1.0597
Epoch 2/2... Discriminator Loss: 1.1203... Generator Loss: 1.8075
Epoch 2/2... Discriminator Loss: 0.9403... Generator Loss: 0.6586
Epoch 2/2... Discriminator Loss: 0.8968... Generator Loss: 0.7137
Epoch 2/2... Discriminator Loss: 1.0566... Generator Loss: 0.5957
Epoch 2/2... Discriminator Loss: 0.8408... Generator Loss: 0.9138
Epoch 2/2... Discriminator Loss: 1.3768... Generator Loss: 1.8618
Epoch 2/2... Discriminator Loss: 1.2561... Generator Loss: 0.4035
Epoch 2/2... Discriminator Loss: 0.9162... Generator Loss: 1.7840
Epoch 2/2... Discriminator Loss: 1.1350... Generator Loss: 0.4458
Epoch 2/2... Discriminator Loss: 0.8181... Generator Loss: 1.2025
Epoch 2/2... Discriminator Loss: 0.8966... Generator Loss: 1.2257
Epoch 2/2... Discriminator Loss: 0.7612... Generator Loss: 0.9666
Epoch 2/2... Discriminator Loss: 0.9645... Generator Loss: 0.8226
Epoch 2/2... Discriminator Loss: 1.1545... Generator Loss: 0.4655
Epoch 2/2... Discriminator Loss: 1.8255... Generator Loss: 2.1385
Epoch 2/2... Discriminator Loss: 1.1747... Generator Loss: 0.5224
Epoch 2/2... Discriminator Loss: 0.8089... Generator Loss: 1.1667
Epoch 2/2... Discriminator Loss: 0.8728... Generator Loss: 0.7455
Epoch 2/2... Discriminator Loss: 0.7701... Generator Loss: 1.0437
Epoch 2/2... Discriminator Loss: 2.5616... Generator Loss: 0.0862
Epoch 2/2... Discriminator Loss: 0.8585... Generator Loss: 0.9802

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [32]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.15

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 4.8042... Generator Loss: 6.2674
Epoch 1/2... Discriminator Loss: 2.3471... Generator Loss: 4.4869
Epoch 1/2... Discriminator Loss: 1.6041... Generator Loss: 2.4444
Epoch 1/2... Discriminator Loss: 1.3689... Generator Loss: 2.0096
Epoch 1/2... Discriminator Loss: 1.7644... Generator Loss: 3.0036
Epoch 1/2... Discriminator Loss: 1.3624... Generator Loss: 1.1670
Epoch 1/2... Discriminator Loss: 1.6478... Generator Loss: 1.3166
Epoch 1/2... Discriminator Loss: 1.3292... Generator Loss: 1.6790
Epoch 1/2... Discriminator Loss: 1.8253... Generator Loss: 2.7830
Epoch 1/2... Discriminator Loss: 1.4932... Generator Loss: 1.9931
Epoch 1/2... Discriminator Loss: 1.3660... Generator Loss: 0.9968
Epoch 1/2... Discriminator Loss: 1.5009... Generator Loss: 0.8918
Epoch 1/2... Discriminator Loss: 1.2676... Generator Loss: 0.7476
Epoch 1/2... Discriminator Loss: 1.3573... Generator Loss: 1.0651
Epoch 1/2... Discriminator Loss: 1.2896... Generator Loss: 1.0607
Epoch 1/2... Discriminator Loss: 1.7125... Generator Loss: 1.3094
Epoch 1/2... Discriminator Loss: 1.3894... Generator Loss: 1.2214
Epoch 1/2... Discriminator Loss: 1.5241... Generator Loss: 0.7824
Epoch 1/2... Discriminator Loss: 1.4597... Generator Loss: 0.9035
Epoch 1/2... Discriminator Loss: 1.4838... Generator Loss: 0.9669
Epoch 1/2... Discriminator Loss: 1.6141... Generator Loss: 1.1340
Epoch 1/2... Discriminator Loss: 1.5322... Generator Loss: 0.2763
Epoch 1/2... Discriminator Loss: 1.4501... Generator Loss: 0.5820
Epoch 1/2... Discriminator Loss: 1.4622... Generator Loss: 0.9036
Epoch 1/2... Discriminator Loss: 1.4079... Generator Loss: 0.6036
Epoch 1/2... Discriminator Loss: 1.4589... Generator Loss: 0.9156
Epoch 1/2... Discriminator Loss: 1.3113... Generator Loss: 0.6764
Epoch 1/2... Discriminator Loss: 1.5165... Generator Loss: 1.0829
Epoch 1/2... Discriminator Loss: 1.4081... Generator Loss: 1.0249
Epoch 1/2... Discriminator Loss: 1.4018... Generator Loss: 0.7162
Epoch 1/2... Discriminator Loss: 1.3906... Generator Loss: 0.6022
Epoch 1/2... Discriminator Loss: 1.5986... Generator Loss: 0.2718
Epoch 1/2... Discriminator Loss: 1.5859... Generator Loss: 0.3305
Epoch 1/2... Discriminator Loss: 1.6727... Generator Loss: 0.2646
Epoch 1/2... Discriminator Loss: 1.4253... Generator Loss: 0.3850
Epoch 1/2... Discriminator Loss: 1.3897... Generator Loss: 0.8071
Epoch 1/2... Discriminator Loss: 1.6156... Generator Loss: 1.1735
Epoch 1/2... Discriminator Loss: 1.3959... Generator Loss: 0.6501
Epoch 1/2... Discriminator Loss: 1.7196... Generator Loss: 0.2142
Epoch 1/2... Discriminator Loss: 1.5119... Generator Loss: 0.3439
Epoch 1/2... Discriminator Loss: 1.5059... Generator Loss: 0.3305
Epoch 1/2... Discriminator Loss: 1.5029... Generator Loss: 0.5825
Epoch 1/2... Discriminator Loss: 1.6517... Generator Loss: 0.2819
Epoch 1/2... Discriminator Loss: 1.4358... Generator Loss: 0.3620
Epoch 1/2... Discriminator Loss: 1.4205... Generator Loss: 0.5423
Epoch 1/2... Discriminator Loss: 1.4062... Generator Loss: 1.1766
Epoch 1/2... Discriminator Loss: 1.3473... Generator Loss: 0.7400
Epoch 1/2... Discriminator Loss: 1.5177... Generator Loss: 0.2975
Epoch 1/2... Discriminator Loss: 1.4569... Generator Loss: 0.3701
Epoch 1/2... Discriminator Loss: 1.4368... Generator Loss: 0.3781
Epoch 1/2... Discriminator Loss: 1.3791... Generator Loss: 0.3477
Epoch 1/2... Discriminator Loss: 1.3754... Generator Loss: 0.4448
Epoch 1/2... Discriminator Loss: 1.5388... Generator Loss: 1.2750
Epoch 1/2... Discriminator Loss: 1.3533... Generator Loss: 0.8457
Epoch 1/2... Discriminator Loss: 1.4242... Generator Loss: 0.3787
Epoch 1/2... Discriminator Loss: 1.3518... Generator Loss: 0.5188
Epoch 1/2... Discriminator Loss: 1.3947... Generator Loss: 0.4927
Epoch 1/2... Discriminator Loss: 1.2500... Generator Loss: 0.7651
Epoch 1/2... Discriminator Loss: 1.2830... Generator Loss: 0.8265
Epoch 1/2... Discriminator Loss: 1.2694... Generator Loss: 0.4945
Epoch 1/2... Discriminator Loss: 1.8315... Generator Loss: 0.1760
Epoch 1/2... Discriminator Loss: 1.3128... Generator Loss: 0.6429
Epoch 1/2... Discriminator Loss: 1.7618... Generator Loss: 0.2143
Epoch 1/2... Discriminator Loss: 1.4500... Generator Loss: 0.8757
Epoch 1/2... Discriminator Loss: 1.2948... Generator Loss: 0.4393
Epoch 1/2... Discriminator Loss: 1.3954... Generator Loss: 0.4717
Epoch 1/2... Discriminator Loss: 1.3616... Generator Loss: 0.3867
Epoch 1/2... Discriminator Loss: 1.5568... Generator Loss: 0.2586
Epoch 1/2... Discriminator Loss: 1.1722... Generator Loss: 0.9647
Epoch 1/2... Discriminator Loss: 1.4099... Generator Loss: 0.9117
Epoch 1/2... Discriminator Loss: 1.6684... Generator Loss: 0.2333
Epoch 1/2... Discriminator Loss: 1.3523... Generator Loss: 0.5912
Epoch 1/2... Discriminator Loss: 1.5579... Generator Loss: 0.2869
Epoch 1/2... Discriminator Loss: 1.3618... Generator Loss: 0.5466
Epoch 1/2... Discriminator Loss: 1.6761... Generator Loss: 1.3968
Epoch 1/2... Discriminator Loss: 1.2733... Generator Loss: 0.7875
Epoch 1/2... Discriminator Loss: 1.1928... Generator Loss: 0.9762
Epoch 1/2... Discriminator Loss: 1.2610... Generator Loss: 0.6720
Epoch 1/2... Discriminator Loss: 1.2162... Generator Loss: 0.9078
Epoch 1/2... Discriminator Loss: 1.4336... Generator Loss: 0.3931
Epoch 1/2... Discriminator Loss: 1.3992... Generator Loss: 0.3900
Epoch 1/2... Discriminator Loss: 1.2628... Generator Loss: 0.7699
Epoch 1/2... Discriminator Loss: 1.4419... Generator Loss: 0.9337
Epoch 1/2... Discriminator Loss: 1.2921... Generator Loss: 0.7989
Epoch 1/2... Discriminator Loss: 1.3364... Generator Loss: 0.4426
Epoch 1/2... Discriminator Loss: 1.4393... Generator Loss: 0.3943
Epoch 1/2... Discriminator Loss: 1.5060... Generator Loss: 0.4140
Epoch 1/2... Discriminator Loss: 1.3724... Generator Loss: 0.8759
Epoch 1/2... Discriminator Loss: 1.3408... Generator Loss: 0.3804
Epoch 1/2... Discriminator Loss: 1.3459... Generator Loss: 1.3271
Epoch 1/2... Discriminator Loss: 1.3118... Generator Loss: 0.5140
Epoch 1/2... Discriminator Loss: 1.2488... Generator Loss: 0.5612
Epoch 1/2... Discriminator Loss: 1.5025... Generator Loss: 0.3101
Epoch 1/2... Discriminator Loss: 1.4527... Generator Loss: 0.3516
Epoch 1/2... Discriminator Loss: 1.3679... Generator Loss: 0.4375
Epoch 1/2... Discriminator Loss: 1.2735... Generator Loss: 1.1938
Epoch 1/2... Discriminator Loss: 1.3923... Generator Loss: 1.1030
Epoch 1/2... Discriminator Loss: 1.3319... Generator Loss: 0.4505
Epoch 1/2... Discriminator Loss: 1.2481... Generator Loss: 0.6839
Epoch 1/2... Discriminator Loss: 1.1265... Generator Loss: 1.3191
Epoch 1/2... Discriminator Loss: 1.3282... Generator Loss: 0.7553
Epoch 1/2... Discriminator Loss: 1.6362... Generator Loss: 0.2559
Epoch 1/2... Discriminator Loss: 1.4256... Generator Loss: 0.3662
Epoch 1/2... Discriminator Loss: 1.4739... Generator Loss: 0.3395
Epoch 1/2... Discriminator Loss: 1.3478... Generator Loss: 0.4554
Epoch 1/2... Discriminator Loss: 1.6343... Generator Loss: 0.2207
Epoch 1/2... Discriminator Loss: 1.2195... Generator Loss: 0.7106
Epoch 1/2... Discriminator Loss: 1.2286... Generator Loss: 0.7134
Epoch 1/2... Discriminator Loss: 1.5619... Generator Loss: 0.2802
Epoch 1/2... Discriminator Loss: 1.3618... Generator Loss: 0.4479
Epoch 1/2... Discriminator Loss: 1.3314... Generator Loss: 0.4896
Epoch 1/2... Discriminator Loss: 1.2551... Generator Loss: 1.0427
Epoch 1/2... Discriminator Loss: 1.5187... Generator Loss: 1.0361
Epoch 1/2... Discriminator Loss: 1.4077... Generator Loss: 0.3847
Epoch 1/2... Discriminator Loss: 1.1963... Generator Loss: 0.8966
Epoch 1/2... Discriminator Loss: 1.3469... Generator Loss: 0.6131
Epoch 1/2... Discriminator Loss: 1.3380... Generator Loss: 0.4351
Epoch 1/2... Discriminator Loss: 1.2950... Generator Loss: 0.4700
Epoch 1/2... Discriminator Loss: 1.2415... Generator Loss: 1.0538
Epoch 1/2... Discriminator Loss: 1.1722... Generator Loss: 0.8700
Epoch 1/2... Discriminator Loss: 1.3467... Generator Loss: 1.5500
Epoch 1/2... Discriminator Loss: 1.5277... Generator Loss: 0.3373
Epoch 1/2... Discriminator Loss: 1.3343... Generator Loss: 0.8484
Epoch 1/2... Discriminator Loss: 1.5460... Generator Loss: 0.2606
Epoch 1/2... Discriminator Loss: 1.6263... Generator Loss: 0.2766
Epoch 1/2... Discriminator Loss: 1.2730... Generator Loss: 1.0740
Epoch 1/2... Discriminator Loss: 1.4290... Generator Loss: 0.3742
Epoch 1/2... Discriminator Loss: 1.4314... Generator Loss: 0.3727
Epoch 1/2... Discriminator Loss: 1.5877... Generator Loss: 0.2942
Epoch 1/2... Discriminator Loss: 1.3349... Generator Loss: 0.7358
Epoch 1/2... Discriminator Loss: 1.3696... Generator Loss: 0.9152
Epoch 1/2... Discriminator Loss: 1.1515... Generator Loss: 0.8468
Epoch 1/2... Discriminator Loss: 1.2777... Generator Loss: 0.7240
Epoch 1/2... Discriminator Loss: 1.2804... Generator Loss: 0.4609
Epoch 1/2... Discriminator Loss: 1.3223... Generator Loss: 0.8827
Epoch 1/2... Discriminator Loss: 1.7812... Generator Loss: 1.5089
Epoch 1/2... Discriminator Loss: 1.2959... Generator Loss: 0.8522
Epoch 1/2... Discriminator Loss: 1.4263... Generator Loss: 1.1138
Epoch 1/2... Discriminator Loss: 1.3681... Generator Loss: 0.3791
Epoch 1/2... Discriminator Loss: 1.4644... Generator Loss: 0.3175
Epoch 1/2... Discriminator Loss: 1.1115... Generator Loss: 0.8967
Epoch 1/2... Discriminator Loss: 1.3704... Generator Loss: 0.4839
Epoch 1/2... Discriminator Loss: 1.2800... Generator Loss: 0.6704
Epoch 1/2... Discriminator Loss: 1.4226... Generator Loss: 0.3734
Epoch 1/2... Discriminator Loss: 1.3752... Generator Loss: 0.3737
Epoch 1/2... Discriminator Loss: 1.4668... Generator Loss: 1.1780
Epoch 1/2... Discriminator Loss: 1.3011... Generator Loss: 0.7593
Epoch 1/2... Discriminator Loss: 1.3090... Generator Loss: 0.9959
Epoch 1/2... Discriminator Loss: 1.1748... Generator Loss: 0.8317
Epoch 1/2... Discriminator Loss: 1.1802... Generator Loss: 0.9621
Epoch 1/2... Discriminator Loss: 1.2054... Generator Loss: 0.6168
Epoch 1/2... Discriminator Loss: 1.3246... Generator Loss: 0.7198
Epoch 1/2... Discriminator Loss: 1.2274... Generator Loss: 0.8463
Epoch 1/2... Discriminator Loss: 1.3163... Generator Loss: 0.5202
Epoch 1/2... Discriminator Loss: 1.6654... Generator Loss: 0.2018
Epoch 1/2... Discriminator Loss: 1.3036... Generator Loss: 0.4928
Epoch 1/2... Discriminator Loss: 1.6967... Generator Loss: 0.2244
Epoch 1/2... Discriminator Loss: 1.2616... Generator Loss: 1.0935
Epoch 2/2... Discriminator Loss: 1.3712... Generator Loss: 0.7053
Epoch 2/2... Discriminator Loss: 1.9525... Generator Loss: 0.1573
Epoch 2/2... Discriminator Loss: 1.4743... Generator Loss: 1.1088
Epoch 2/2... Discriminator Loss: 1.4288... Generator Loss: 0.4247
Epoch 2/2... Discriminator Loss: 1.3394... Generator Loss: 0.4786
Epoch 2/2... Discriminator Loss: 1.2897... Generator Loss: 0.5093
Epoch 2/2... Discriminator Loss: 1.5902... Generator Loss: 0.2397
Epoch 2/2... Discriminator Loss: 1.3106... Generator Loss: 0.4718
Epoch 2/2... Discriminator Loss: 1.7542... Generator Loss: 0.2021
Epoch 2/2... Discriminator Loss: 1.3383... Generator Loss: 0.4532
Epoch 2/2... Discriminator Loss: 1.5122... Generator Loss: 0.2876
Epoch 2/2... Discriminator Loss: 1.2959... Generator Loss: 0.4859
Epoch 2/2... Discriminator Loss: 1.3211... Generator Loss: 0.8394
Epoch 2/2... Discriminator Loss: 1.3318... Generator Loss: 0.5567
Epoch 2/2... Discriminator Loss: 1.6376... Generator Loss: 0.2595
Epoch 2/2... Discriminator Loss: 1.3459... Generator Loss: 0.4097
Epoch 2/2... Discriminator Loss: 1.1599... Generator Loss: 0.9780
Epoch 2/2... Discriminator Loss: 1.3305... Generator Loss: 0.7179
Epoch 2/2... Discriminator Loss: 1.3525... Generator Loss: 0.4139
Epoch 2/2... Discriminator Loss: 1.2792... Generator Loss: 0.8775
Epoch 2/2... Discriminator Loss: 1.4425... Generator Loss: 0.3190
Epoch 2/2... Discriminator Loss: 1.4068... Generator Loss: 0.4701
Epoch 2/2... Discriminator Loss: 1.2587... Generator Loss: 0.8195
Epoch 2/2... Discriminator Loss: 1.3398... Generator Loss: 1.0444
Epoch 2/2... Discriminator Loss: 1.3543... Generator Loss: 0.4460
Epoch 2/2... Discriminator Loss: 1.2097... Generator Loss: 0.9168
Epoch 2/2... Discriminator Loss: 1.5215... Generator Loss: 0.2967
Epoch 2/2... Discriminator Loss: 1.0542... Generator Loss: 0.5528
Epoch 2/2... Discriminator Loss: 1.4385... Generator Loss: 1.0621
Epoch 2/2... Discriminator Loss: 1.3079... Generator Loss: 0.4652
Epoch 2/2... Discriminator Loss: 1.3550... Generator Loss: 0.3910
Epoch 2/2... Discriminator Loss: 1.3003... Generator Loss: 0.5215
Epoch 2/2... Discriminator Loss: 1.4974... Generator Loss: 0.3187
Epoch 2/2... Discriminator Loss: 1.2658... Generator Loss: 0.4563
Epoch 2/2... Discriminator Loss: 1.4105... Generator Loss: 0.3328
Epoch 2/2... Discriminator Loss: 1.3263... Generator Loss: 0.4805
Epoch 2/2... Discriminator Loss: 1.4740... Generator Loss: 1.0239
Epoch 2/2... Discriminator Loss: 1.4722... Generator Loss: 1.2174
Epoch 2/2... Discriminator Loss: 1.2514... Generator Loss: 0.7239
Epoch 2/2... Discriminator Loss: 1.4840... Generator Loss: 0.2944
Epoch 2/2... Discriminator Loss: 1.5408... Generator Loss: 0.3161
Epoch 2/2... Discriminator Loss: 1.5535... Generator Loss: 0.2805
Epoch 2/2... Discriminator Loss: 1.5864... Generator Loss: 0.2404
Epoch 2/2... Discriminator Loss: 1.3007... Generator Loss: 0.4476
Epoch 2/2... Discriminator Loss: 1.4006... Generator Loss: 0.3414
Epoch 2/2... Discriminator Loss: 1.4602... Generator Loss: 0.3009
Epoch 2/2... Discriminator Loss: 1.7301... Generator Loss: 0.2102
Epoch 2/2... Discriminator Loss: 1.4907... Generator Loss: 0.3213
Epoch 2/2... Discriminator Loss: 1.4102... Generator Loss: 0.3569
Epoch 2/2... Discriminator Loss: 1.3495... Generator Loss: 0.3685
Epoch 2/2... Discriminator Loss: 1.3718... Generator Loss: 0.4299
Epoch 2/2... Discriminator Loss: 1.2238... Generator Loss: 0.6068
Epoch 2/2... Discriminator Loss: 1.2629... Generator Loss: 0.9679
Epoch 2/2... Discriminator Loss: 1.4010... Generator Loss: 0.9870
Epoch 2/2... Discriminator Loss: 1.2896... Generator Loss: 0.7538
Epoch 2/2... Discriminator Loss: 1.3146... Generator Loss: 0.5098
Epoch 2/2... Discriminator Loss: 1.5068... Generator Loss: 0.2768
Epoch 2/2... Discriminator Loss: 1.3541... Generator Loss: 0.4597
Epoch 2/2... Discriminator Loss: 1.3379... Generator Loss: 0.3825
Epoch 2/2... Discriminator Loss: 1.3681... Generator Loss: 0.4031
Epoch 2/2... Discriminator Loss: 1.2424... Generator Loss: 0.8849
Epoch 2/2... Discriminator Loss: 1.2517... Generator Loss: 0.8216
Epoch 2/2... Discriminator Loss: 1.3090... Generator Loss: 0.5785
Epoch 2/2... Discriminator Loss: 1.2357... Generator Loss: 0.7489
Epoch 2/2... Discriminator Loss: 1.3372... Generator Loss: 0.3521
Epoch 2/2... Discriminator Loss: 1.3979... Generator Loss: 0.3647
Epoch 2/2... Discriminator Loss: 1.1979... Generator Loss: 0.5150
Epoch 2/2... Discriminator Loss: 1.2558... Generator Loss: 0.6884
Epoch 2/2... Discriminator Loss: 1.2257... Generator Loss: 0.5214
Epoch 2/2... Discriminator Loss: 1.4591... Generator Loss: 0.2810
Epoch 2/2... Discriminator Loss: 1.4677... Generator Loss: 0.3320
Epoch 2/2... Discriminator Loss: 1.3122... Generator Loss: 0.6004
Epoch 2/2... Discriminator Loss: 1.5018... Generator Loss: 0.2891
Epoch 2/2... Discriminator Loss: 1.6235... Generator Loss: 0.2218
Epoch 2/2... Discriminator Loss: 1.4956... Generator Loss: 0.3145
Epoch 2/2... Discriminator Loss: 1.4674... Generator Loss: 0.3613
Epoch 2/2... Discriminator Loss: 1.3674... Generator Loss: 1.0028
Epoch 2/2... Discriminator Loss: 1.3849... Generator Loss: 0.3820
Epoch 2/2... Discriminator Loss: 1.4929... Generator Loss: 0.3050
Epoch 2/2... Discriminator Loss: 1.2577... Generator Loss: 0.4996
Epoch 2/2... Discriminator Loss: 1.3665... Generator Loss: 0.4837
Epoch 2/2... Discriminator Loss: 1.3912... Generator Loss: 0.4321
Epoch 2/2... Discriminator Loss: 1.4690... Generator Loss: 0.2720
Epoch 2/2... Discriminator Loss: 1.6164... Generator Loss: 0.2404
Epoch 2/2... Discriminator Loss: 1.5320... Generator Loss: 0.2995
Epoch 2/2... Discriminator Loss: 1.3127... Generator Loss: 0.9073
Epoch 2/2... Discriminator Loss: 1.0534... Generator Loss: 0.8396
Epoch 2/2... Discriminator Loss: 1.3072... Generator Loss: 0.8576
Epoch 2/2... Discriminator Loss: 1.3609... Generator Loss: 0.4731
Epoch 2/2... Discriminator Loss: 1.4455... Generator Loss: 0.3176
Epoch 2/2... Discriminator Loss: 1.2235... Generator Loss: 0.5463
Epoch 2/2... Discriminator Loss: 1.4284... Generator Loss: 0.2875
Epoch 2/2... Discriminator Loss: 1.2891... Generator Loss: 1.0060
Epoch 2/2... Discriminator Loss: 1.2342... Generator Loss: 0.5410
Epoch 2/2... Discriminator Loss: 1.4863... Generator Loss: 0.3250
Epoch 2/2... Discriminator Loss: 1.2160... Generator Loss: 0.7228
Epoch 2/2... Discriminator Loss: 1.3072... Generator Loss: 0.7317
Epoch 2/2... Discriminator Loss: 1.2473... Generator Loss: 0.6388
Epoch 2/2... Discriminator Loss: 1.3022... Generator Loss: 0.9814
Epoch 2/2... Discriminator Loss: 1.3804... Generator Loss: 0.9799
Epoch 2/2... Discriminator Loss: 1.2826... Generator Loss: 0.5584
Epoch 2/2... Discriminator Loss: 1.4464... Generator Loss: 0.3636
Epoch 2/2... Discriminator Loss: 1.3969... Generator Loss: 0.3231
Epoch 2/2... Discriminator Loss: 1.2068... Generator Loss: 0.5514
Epoch 2/2... Discriminator Loss: 1.4681... Generator Loss: 0.3268
Epoch 2/2... Discriminator Loss: 1.2868... Generator Loss: 0.6824
Epoch 2/2... Discriminator Loss: 1.3146... Generator Loss: 0.4738
Epoch 2/2... Discriminator Loss: 1.5924... Generator Loss: 0.2584
Epoch 2/2... Discriminator Loss: 1.3869... Generator Loss: 0.3257
Epoch 2/2... Discriminator Loss: 1.3536... Generator Loss: 0.4495
Epoch 2/2... Discriminator Loss: 1.3866... Generator Loss: 0.4333
Epoch 2/2... Discriminator Loss: 1.1020... Generator Loss: 0.6477
Epoch 2/2... Discriminator Loss: 1.3519... Generator Loss: 0.5027
Epoch 2/2... Discriminator Loss: 1.5632... Generator Loss: 0.2861
Epoch 2/2... Discriminator Loss: 1.4138... Generator Loss: 0.3882
Epoch 2/2... Discriminator Loss: 1.1984... Generator Loss: 0.7236
Epoch 2/2... Discriminator Loss: 1.3025... Generator Loss: 0.5866
Epoch 2/2... Discriminator Loss: 1.6018... Generator Loss: 0.2378
Epoch 2/2... Discriminator Loss: 1.3988... Generator Loss: 0.4031
Epoch 2/2... Discriminator Loss: 1.4289... Generator Loss: 0.3274
Epoch 2/2... Discriminator Loss: 1.4896... Generator Loss: 0.3031
Epoch 2/2... Discriminator Loss: 1.4870... Generator Loss: 0.3286
Epoch 2/2... Discriminator Loss: 1.3719... Generator Loss: 0.8192
Epoch 2/2... Discriminator Loss: 1.3770... Generator Loss: 0.3959
Epoch 2/2... Discriminator Loss: 1.4652... Generator Loss: 0.3783
Epoch 2/2... Discriminator Loss: 1.1997... Generator Loss: 0.7129
Epoch 2/2... Discriminator Loss: 1.4831... Generator Loss: 0.3622
Epoch 2/2... Discriminator Loss: 1.3343... Generator Loss: 0.5710
Epoch 2/2... Discriminator Loss: 1.3005... Generator Loss: 0.6997
Epoch 2/2... Discriminator Loss: 1.2425... Generator Loss: 0.6766
Epoch 2/2... Discriminator Loss: 1.5436... Generator Loss: 0.2535
Epoch 2/2... Discriminator Loss: 1.3588... Generator Loss: 0.3956
Epoch 2/2... Discriminator Loss: 1.3002... Generator Loss: 0.4657
Epoch 2/2... Discriminator Loss: 1.6813... Generator Loss: 0.2362
Epoch 2/2... Discriminator Loss: 1.3395... Generator Loss: 0.4029
Epoch 2/2... Discriminator Loss: 1.2767... Generator Loss: 0.6805
Epoch 2/2... Discriminator Loss: 1.2392... Generator Loss: 0.4476
Epoch 2/2... Discriminator Loss: 1.3762... Generator Loss: 1.0848
Epoch 2/2... Discriminator Loss: 1.1967... Generator Loss: 0.6348
Epoch 2/2... Discriminator Loss: 1.2754... Generator Loss: 0.4722
Epoch 2/2... Discriminator Loss: 1.2629... Generator Loss: 0.5077
Epoch 2/2... Discriminator Loss: 1.4485... Generator Loss: 0.3781
Epoch 2/2... Discriminator Loss: 1.2856... Generator Loss: 0.6500
Epoch 2/2... Discriminator Loss: 1.3882... Generator Loss: 0.4164
Epoch 2/2... Discriminator Loss: 1.5337... Generator Loss: 0.2788
Epoch 2/2... Discriminator Loss: 1.3675... Generator Loss: 0.3483
Epoch 2/2... Discriminator Loss: 1.2757... Generator Loss: 0.4941
Epoch 2/2... Discriminator Loss: 1.7981... Generator Loss: 0.1936
Epoch 2/2... Discriminator Loss: 1.4608... Generator Loss: 0.3666
Epoch 2/2... Discriminator Loss: 1.5539... Generator Loss: 0.2802
Epoch 2/2... Discriminator Loss: 1.2464... Generator Loss: 0.5633
Epoch 2/2... Discriminator Loss: 1.4011... Generator Loss: 0.3736
Epoch 2/2... Discriminator Loss: 1.4827... Generator Loss: 0.2905
Epoch 2/2... Discriminator Loss: 1.4396... Generator Loss: 0.3709
Epoch 2/2... Discriminator Loss: 1.3224... Generator Loss: 0.4237
Epoch 2/2... Discriminator Loss: 1.1976... Generator Loss: 0.7083
Epoch 2/2... Discriminator Loss: 1.6121... Generator Loss: 0.2445
Epoch 2/2... Discriminator Loss: 1.1090... Generator Loss: 0.7239

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.